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Record W4285086249 · doi:10.3389/fvets.2022.922992

Plasma Cytokines and Birth Weight as Biomarkers of Vaccine-Induced Humoral Responses in Piglets

2022· article· en· W4285086249 on OpenAlexafffund
Sean Lipsit, Antonio Facciuolo, Erin Scruten, Philip Griebel, Scott Napper

Bibliographic record

VenueFrontiers in Veterinary Science · 2022
Typearticle
Languageen
FieldImmunology and Microbiology
TopicMicrobial infections and disease research
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of CanadaInnovation Saskatchewan
KeywordsVaccinationImmunologySeroconversionImmune systemMedicineImmunityTiterCytokineAntibody

Abstract

fetched live from OpenAlex

Failure to mount an effective immune response to vaccination leaves individuals at risk for infection and can compromise herd immunity. Vaccine unresponsiveness can range from poor responses “low responders” to a failure to seroconvert “non-responders.” Biomarkers of vaccine unresponsiveness, particularly those measured at the time of vaccination, could facilitate more strategic vaccination programs. We previously reported that pro-inflammatory cytokine signaling within peripheral blood mononuclear cells, elevated plasma interferon-gamma (IFNγ), and low birth weight correlated with vaccine-induced serum IgG titers in piglets that were below the threshold of detectable seroconversion (vaccine non-responders). These observations suggested that plasma IFNγ concentration and birth weight might serve as pre-vaccination biomarkers of vaccine unresponsiveness. To test this hypothesis, piglets (n = 67) from a different production facility were vaccinated with the same commercial Mycoplasma hyopneumoniae bacterin (RespiSure-One) to determine if there was a consistent and significant association between vaccine-induced serum IgG titers and either plasma cytokine concentrations or birth weight. All piglets seroconverted following vaccination with significantly less variability in vaccine-induced serum IgG titers than observed in the previous vaccine trial. Piglets exhibited highly variable birth weights and plasma cytokine concentrations prior to vaccination, but there were no significant associations (p > 0.05) between these variables and vaccine-induced serum IgG titers. There were significant (p < 0.001) differences in plasma IFNγ concentrations among individual litters (n = 6), and plasma IFNγ concentrations decreased in all pigs from birth to 63-days of age. One of the six litters (n = 11 piglets) exhibited significantly elevated plasma IFNγ concentrations during the first 3 weeks of life (p < 0.001) and at the time of vaccination (p < 0.01). This litter, however, had similar vaccine-induced serum IgG titers when compared to the other piglets in this study. Collectively the two studies indicate that while plasma cytokines and birth weight can be associated with vaccine non-responsiveness, their temporal and individual variation, as well as the complexity of the vaccine responsiveness phenotype, make them inconsistent biomarkers for predicting the less extreme phenotype of vaccine low responders.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.021
GPT teacher head0.289
Teacher spread0.268 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2022
Admission routes2
Has abstractyes

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